Patrick Walters

Bharath Ramsundar

Vijay Pande

Peter Eastman

With much success already attributed to deep learning, this discipline has started making waves throughout science broadly and the life sciences in particular. With this practical book, developers and scientists will learn how deep learning is used for genomics, chemistry, biophysics, microscopy, medical analysis, drug discovery, and other fields. As a running case study, the authors focus on the problem of designing new therapeutics, one of science's greatest challenges because this practice ties together physics, chemistry, biology and medicine. Using TensorFlow and the DeepChem library, this book introduces deep network primitives including image convolutional networks, 1D convolutions for genomics, graph convolutions for molecular graphs, atomic convolutions for molecular structures, and molecular autoencoders. Deep Learning for the Life Sciences is ideal for practicing developers interested in applying their skills to scientific applications such as biology, genetics, and drug discovery, as well as scientists interested in adding deep learning to their core skills.

Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

Deep Learning for the Life Sciences

Reza Bosagh Zadeh

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units

From Linear Regression to Reinforcement Learning

TensorFlow for Deep Learning

Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working

Bharath Ramsundar

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